Technical Architect

Overview ROLE : The Technical Architect is the engineering owner of the product. They receive business requirements from the Product Architect and translate them into coherent technical strategies - choosing the right technology stack, defining system design, and leading the engineering team to build products that are scalable, secure, and future-ready. Within Prodapt's AI-native portfolio, this role is the custodian of platform integrity and the technical compass for all engineering decisions. Responsibilities CORE RESPONSIBILITIES • Own end-to-end technical architecture across core, AI/ML platform, data infrastructure, APIs, and integration layers. • Translate product requirements (PRDs) into detailed technical designs, system diagrams, & engineering specifications. • Make authoritative technology choices: frameworks, cloud providers, data stores, AI/LLM orchestration tooling with clear rationale. • Define and enforce engineering standards: coding guidelines, security posture, performance SLOs, and CI/CD practices. • Lead, mentor, and grow a team of Forward Deployment Engineers - conduct architecture and code reviews. • Identify and mitigate technical risks early; build resilience and observability into every product layer. • Collaborate with Product Architect on feasibility, trade-off analysis, and build-vs-buy decisions. • Drive prototype-to-production engineering excellence; own technical debt strategy and platform evolution roadmap. MUST-HAVE SKILLS & EXPERIENCE • Strong product engineering experience in one of the platforms: ServiceNow, Salesforce, Netcracker, Amdocs, Ericson. • Expert-level software engineering in Python, Go, or Java with strong polyglot instincts across the stack. • Deep experience designing distributed systems, microservices, and event-driven architectures at scale. • Hands-on AI/ML platform engineering: LLM integration, vector databases, RAG pipelines, model serving (vLLM, Ollama, TensorRT-LLM, Triton, etc.). • Cloud-native expertise across AWS / Azure / GCP: Kubernetes, Terraform, observability stacks, and FinOps. • Strong data engineering background: streaming (Kafka/Flink), warehousing (Snowflake/BigQuery), and lineage tooling. • Security-first mindset: zero-trust networking, secrets management, vulnerability remediation at the infra layer. MINDSET & BEHAVIOURS • Pragmatic visionary - balances architectural elegance with delivery speed and operational reality. Requirements MUST-HAVE SKILLS & EXPERIENCE • Strong product engineering experience in one of the platforms: ServiceNow, Salesforce, Netcracker, Amdocs, Ericson. • Expert-level software engineering in Python, Go, or Java with strong polyglot instincts across the stack. • Deep experience designing distributed systems, microservices, and event-driven architectures at scale. • Hands-on AI/ML platform engineering: LLM integration, vector databases, RAG pipelines, model serving (vLLM, Ollama, TensorRT-LLM, Triton, etc.). • Cloud-native expertise across AWS / Azure / GCP: Kubernetes, Terraform, observability stacks, and FinOps. • Strong data engineering background: streaming (Kafka/Flink), warehousing (Snowflake/BigQuery), and lineage tooling. • Security-first mindset: zero-trust networking, secrets management, vulnerability remediation at the infra layer. MINDSET & BEHAVIOURS • Pragmatic visionary - balances architectural elegance with delivery speed and operational reality. • Trusted technical authority - engineers follow their lead; can say no with reasoned alternatives. • Systems thinker - sees second-order effects of technology choices on cost, scalability, and team velocity. • Continuous learner - actively evaluates emerging AI/ML tooling and brings proven innovations to the team. PREFERRED QUALIFICATIONS • B.S. / M.S. in Computer Science, Systems Engineering, or related field. • Published architecture patterns, open-source contributions, or conference talks in AI/infra domain. • Experience as a technical lead in a telecom, network automation, or enterprise SaaS environment.

CORE RESPONSIBILITIES • Own end-to-end technical architecture across core, AI/ML platform, data infrastructure, APIs, and integration layers. • Translate product requirements (PRDs) into detailed technical designs, system diagrams, & engineering specifications. • Make authoritative technology choices: frameworks, cloud providers, data stores, AI/LLM orchestration tooling with clear rationale. • Define and enforce engineering standards: coding guidelines, security posture, performance SLOs, and CI/CD practices. • Lead, mentor, and grow a team of Forward Deployment Engineers - conduct architecture and code reviews. • Identify and mitigate technical risks early; build resilience and observability into every product layer. • Collaborate with Product Architect on feasibility, trade-off analysis, and build-vs-buy decisions. • Drive prototype-to-production engineering excellence; own technical debt strategy and platform evolution roadmap. MUST-HAVE SKILLS & EXPERIENCE • Strong product engineering experience in one of the platforms: ServiceNow, Salesforce, Netcracker, Amdocs, Ericson. • Expert-level software engineering in Python, Go, or Java with strong polyglot instincts across the stack. • Deep experience designing distributed systems, microservices, and event-driven architectures at scale. • Hands-on AI/ML platform engineering: LLM integration, vector databases, RAG pipelines, model serving (vLLM, Ollama, TensorRT-LLM, Triton, etc.). • Cloud-native expertise across AWS / Azure / GCP: Kubernetes, Terraform, observability stacks, and FinOps. • Strong data engineering background: streaming (Kafka/Flink), warehousing (Snowflake/BigQuery), and lineage tooling. • Security-first mindset: zero-trust networking, secrets management, vulnerability remediation at the infra layer. MINDSET & BEHAVIOURS • Pragmatic visionary - balances architectural elegance with delivery speed and operational reality.

MUST-HAVE SKILLS & EXPERIENCE • Strong product engineering experience in one of the platforms: ServiceNow, Salesforce, Netcracker, Amdocs, Ericson. • Expert-level software engineering in Python, Go, or Java with strong polyglot instincts across the stack. • Deep experience designing distributed systems, microservices, and event-driven architectures at scale. • Hands-on AI/ML platform engineering: LLM integration, vector databases, RAG pipelines, model serving (vLLM, Ollama, TensorRT-LLM, Triton, etc.). • Cloud-native expertise across AWS / Azure / GCP: Kubernetes, Terraform, observability stacks, and FinOps. • Strong data engineering background: streaming (Kafka/Flink), warehousing (Snowflake/BigQuery), and lineage tooling. • Security-first mindset: zero-trust networking, secrets management, vulnerability remediation at the infra layer. MINDSET & BEHAVIOURS • Pragmatic visionary - balances architectural elegance with delivery speed and operational reality. • Trusted technical authority - engineers follow their lead; can say no with reasoned alternatives. • Systems thinker - sees second-order effects of technology choices on cost, scalability, and team velocity. • Continuous learner - actively evaluates emerging AI/ML tooling and brings proven innovations to the team. PREFERRED QUALIFICATIONS • B.S. / M.S. in Computer Science, Systems Engineering, or related field. • Published architecture patterns, open-source contributions, or conference talks in AI/infra domain. • Experience as a technical lead in a telecom, network automation, or enterprise SaaS environment.